Voice-focused deep learning tech firm Syntiant has notched $35 million in a Series C funding round. Applied Ventures, LLC and M12, the venture fund of Microsoft, headed up in the financing, according to a Tuesday (August 4) announcement from Syntiant.Alpha Edison, Miramar Digital Ventures and Atlantic Bridge Capital also came on board as new investors in Syntiant.Syntiant CEO Kurt Busch said in the announcement, "It is a tremendous honor to know that some of the world's leading tech investors are supporting our growth stage, as we deliver our deep learning voice solution to customers across the globe."Syntiant, Its neural decision processors (NDPs) have become a part of the design of a broad array of devices powered by batteries like smart speakers, notebooks, headphones and smartphones.M12 Managing Director Samir Kumar said in the announcement, "Syntiant's class-leading power performance is making an ambient fabric of neural network-powered intelligence at the tiny edge a reality."According to Syntiant, the market for speech and voice recognition is anticipated to arrive at $26.8 billion by 2025, citing a Meticulous Research …

In June, OpenAI unveiled the largest language model in the world, a text-generating tool called GPT-3 that can write creative fiction, translate legalese into plain English, and answer obscure trivia questions. It's the latest feat of intelligence achieved by deep learning, a machine learning method patterned after the way neurons in the brain process and store information. But it came at a hefty price: at least $4.6 million and 355 years in computing time, assuming the model was trained on a standard neural network chip, or GPU. The model's colossal size -- 1,000 times larger than a typical language model -- is the main factor in its high cost. "You have to throw a lot more computation at something to get a little improvement in performance," says Neil Thompson, an MIT researcher who has tracked deep learning's unquenchable thirst for computing.

What's in this issue: Here, I try to deconstruct the buzz about GPT-3, and in trying to do that, I dig deeper into what hype means in the context of emergent technologies and how to integrate the noise out while consuming new science on social media. Read the rest of the post for a framework to think about the buzz in breakthrough technologies while living in the midst of it. GPT-3 or similar models did not assist in any of this writing. If you're reading this over email, it might be best to read it directly on substack as some email clients clip long emails and block images used as illustration. If you are a new visitor for the Page Street Labs newsletter, check out our hello world post explaining what this newsletter is about and why we exist. Words have no "grounded" meanings unless you also take the full context of the reader and the writer, and yet we use words to get to that wordless essence with strangers we will never know. This was in full display when GPT-3 went viral, at least in Tech Twitter, over last weekend. Many researchers, including myself, used the words "GPT-3" and "hype" in the same Tweet to contain people's expectations.

Training and testing deep learning models is a difficult process that requires sophisticated knowledge of machine learning and data infrastructures. From feature modeling to hyperparameter optimization, the processes for training and testing deep learning models are one of the biggest bottlenecks in data science solutions in the real world. Simplifying this element could help to streamline the adoption of deep learning technologies. While the low-code training of deep learning models is a nascent space, we are already seeing relevant innovations. One of the most complete solutions to tackle that problem came from Uber AI Labs.

The growth of the internet due to social networks such as Facebook, Twitter, Linkedin, Instagram etc. has led to significant users interaction and has empowered users to express their opinions about products, services, events, their preferences among others. It has also provided opportunities to the users to share their wisdom and experiences with each other. The faster development of social networks is causing explosive growth of digital content. It has turned online opinions, blogs, tweets, and posts into a very valuable asset for the corporates to get insights from the data and plan their strategy. Business organizations need to process and study these sentiments to investigate data and to gain business insights(Yadav & Vishwakarma, 2020).

First, you must know that the sun is actually a cat. Also, you must know that the sun is actually not a cat. Over the past couple of weeks, the ML community had their handsful discussing and displaying the wide range of utilities of GPT-3. Many developers, both professionals and amateurs, have expressed their surprise saying how most of the demos generated using GPT-3 in a few minutes would usually require significant engineering effort and machine learning expertise. In the next section, we list 15 exciting ways in which GPT-3 has been leveraged.

The fervor around state-of-the-art AI language models like OpenAI's GPT-3 hasn't died down. Melanie Mitchell, a professor of computer science at Portland State University, found evidence that GPT-3 can make primitive analogies. Raphaël Millière, a philosopher of mind and cognitive science at Columbia University's Center for Science and Society, asked GPT-3 to compose a response to the philosophical essays written about it. Among other applications, the API providing access to the model has been used to create a recipe generator, an all-purpose Excel function, and a comedy sketch writer. But even language models as powerful as GPT-3 have limitations that remain unaddressed.

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Content provided by the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences. Assessment of skeletal maturity is an important tool in managing human's growth problems, especially in assisting physicians decide the best treatment for various skeletal disorders. This task remains challenging when using machine learning method due to limited data and large anatomical variations among different subjects. Recently, researchers from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences and University of Hong Kong introduced an ensemble-based deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left-hand radiographs. The study was published in IEEE Transactions on Systems, Man, and Cybernetics: Systems. The researchers combined the dense connection mechanism with the ensemble model to improve the stability and accuracy of a skeletal maturity assessment system.

In recent years, deep learning models have produced a substantial amount of advances in various areas, including computer vision. Computer vision typically usually works by analysing images that have been captured using the physical camera sensor, followed by a human-in-the-loop process that requires annotators to label things of interest. It's important to note that the more sophisticated the annotation is, the more laborious labelling can be. But it provides for a much richer analysis of the image itself. For example, for spotting a tiny detail within an image, a simple bounding box around the object might suffice. But once you start looking to get a robot to grasp something, you might need a segmentation mask to flesh out the fine contours of the object.